Sensor selection to improve estimates of particulate matter concentration from a low-cost network

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dc.contributor.authorSousan, Sinan
dc.contributor.authorGray, Alyson
dc.contributor.authorZuidema, Christopher
dc.contributor.authorStebounova, Larissa
dc.contributor.authorThomas, Geb
dc.contributor.authorKoehler, Kirsten
dc.contributor.authorPeters, Thomas
dc.date.accessioned2019-06-20T19:29:00Z
dc.date.available2019-06-20T19:29:00Z
dc.date.issued2018-09-08
dc.description.abstractDeployment of low-cost sensors in the field is increasingly popular. However, each sensor requires on-site calibration to increase the accuracy of the measurements. We established a laboratory method, the Average Slope Method, to select sensors with similar response so that a single, on-site calibration for one sensor can be used for all other sensors. The laboratory method was performed with aerosolized salt. Based on linear regression, we calculated slopes for 100 particulate matter (PM) sensors, and 50% of the PM sensors fell within ±14% of the average slope. We then compared our Average Slope Method with an Individual Slope Method and concluded that our first method balanced convenience and precision for our application. Laboratory selection was tested in the field, where we deployed 40 PM sensors inside a heavy-manufacturing site at spatially optimal locations and performed a field calibration to calculate a slope for three PM sensors with a reference instrument at one location. The average slope was applied to all PM sensors for mass concentration calculations. The calculated percent differences in the field were similar to the laboratory results. Therefore, we established a method that reduces the time and cost associated with calibration of low-cost sensors in the field.en_US
dc.description.sponsorshipECU Open Access Publishing Support Funden_US
dc.identifier.citationSousan, S., Gray, A., Zuidema, C., Stebounova, L., Thomas, G., Koehler, K., & Peters, T. (2018). Sensor selection to improve estimates of particulate matter concentration from a low-cost network. Sensors (Basel, Switzerland), 18(9) doi:10.3390/s18093008en_US
dc.identifier.doi10.3390/s18093008
dc.identifier.otherPMC6163282
dc.identifier.pmid30205550en_US
dc.identifier.urihttp://hdl.handle.net/10342/7358
dc.language.isoen_USen_US
dc.relation.urihttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6163282/en_US
dc.subjectPMen_US
dc.subjectaerosol exposureen_US
dc.subjectlow-cost sensorsen_US
dc.subjectlow-cost wireless networken_US
dc.subjectoccupational monitoringen_US
dc.subjectsensor calibrationen_US
dc.subjectsensor selectionen_US
dc.titleSensor selection to improve estimates of particulate matter concentration from a low-cost networken_US
dc.typeArticleen_US
ecu.journal.issue9en_US
ecu.journal.nameSensorsen_US
ecu.journal.volume18en_US

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